基于测距红外传感器的轮式迷宫机器人设计

本设计基于轮式迷宫机器人红外检测系统多采用5组或6组一体式红外接收传感器,无法进行测距,设计者使用了4组测距式红外传感器的轮式迷宫机器人。给出了电路设计、传感器布局、直行姿态调整以及快速过弯的方案。实验结果表明,该设计方案可行,轮式迷宫机器人工作稳定可靠,速度较快。     

基于红外传感器的两轮桌面机器人设计实现

为了验证红外传感器作为姿态检测的可行性,又能满足经济性要求,设计了一种基于红外传感器的两轮桌面式小型机器人WillBot-Ⅱ,此机器人通过红外传感器检测与地面之间的距离来达到姿态检测的目的,此种方法大大节约了传感器的成本,同时克服了零点漂移现象,能够有效准确地对姿态进行检测.首先介绍了机器人的系统构成,包括机械结构部分...     

基于红外传感器的移动微机器人定位研究

综合概率论的思想,采用基于红外传感器的微机器人粒子滤波定位方法在已知环境中实现微机器人的自定位.因微机器人受尺寸效应影响无法安装位置传感器,故采用计步的方法存储机器人的运动信息.观测模型采用适合小尺寸微型机器人的红外测距地图匹配方式.因微机器人的存储能力有限,故采用分块存储地图的方法,便于微机器人快速加载地图而完成地图匹配,从而加速其定位过程.实验验证了微机器人的定位性能.     

局部地形变化检测与移动机器人的行为决策

针对非结构化、未知工作环境下机器人避障／越障等运动需求,提出一种基于红外传感器的局部地形变化检测方法。分析了局部地形变化的21种基本情况,提出并比较了基于机构和基于传感器的两种检测方法,推导了地形坡度变化的计算公式,给出了实际地形组合变化时机器人的具体行为策略,在此基础上进行基于红外传感器的局部地形变化实验,为机器人在未知环境下安全可靠的运动提供了前提和保障。     

基于神经网络的自主吸尘机器人混合视觉研究

针对自主吸尘机器人非结构化的工作环境及避障的实时性要求,提出融合了超声波传感器和红外传感器的混合视觉算法,并且基于BP神经网络的传感器信息融合技术进行了实验。     

红外/雷达双模导引头融合器硬件及软件实现

基于单传感器(雷达或红外)的系统存在着局限性。介绍了基于多传感器(雷达和红外)信号融合的目标识别和跟踪系统,它能利用不同传感器的数据互补和冗余。主要讨论了利用DSP处理芯片为核心的控制器的硬件设计与软件的实现。该控制器利用雷达系统与红外系统的信号对导弹进行控制。在较远距离时,该控制器利用雷达系统的信号对导引头进行控制,同时利用该信号对红外伺服系统进行控制,在较近距离时,当红外系统在控制下稳定跟踪目标后,该设备将原先的雷达系统控制信号转换为更精确的红外系统控制信号。     

基于相对定位的多移动微机器人协作定位方法

为了提高微机器人定位的精确性,研究了基于相对定位的多移动微机器人协作定位方法.结合微机器人尺寸小的特点,定位系统中采用低功耗的红外传感器作为微机器人的测距传感器,利用粒子滤波实现微机器人的自定位.在自定位及相对定位的基础上,提出了多微机器人的协作定位算法,通过保留一定数目的粒子在自身粒子集合中,而非交换全部的粒子的策略,保证了一定的定位精度.仿真与实验结果验证了协作定位算法的有效性.     

移动机器人的多传感器测距系统设计

在移动机器人的路径规划过程中,必须掌握障碍物的距离信息.基于超声波和红外传感器的测距原理,设计了一种移动机器人多传感器测距系统,可测量0～200 cm距离内存在的障碍物,测量误差小于1 %.采用超声波和红外2种传感器组成3组测距采集系统,采集机器人3个不同方位的障碍物信息,解决了单一传感器测距盲区的问题,并详细介绍了该系统的软件和硬件设计.     

迷宫机器人中使用红外传感器测距的研究

本文介绍一种使用红外传感器快速测量迷宫机器人与迷宫隔墙之间的距离并进行墙壁判断和直行姿态调整的方法,给出了相应的电路设计和软件设计方案.试验结果表明,设计方案可行,迷宫机器人工作稳定、可靠.     

基于红外火焰传感器和最小二乘法的灭火机器人

设计是基于红外火焰传感器和最小二乘法的灭火机器人,可以比较准确地定位着火点并进行灭火.系统硬件以STC12C5A为核心,在传统避障机器人的基础上加上了红外火焰传感器模块和灭火模块.在软件上采用最小二乘法进行曲线拟合,得到AD值和距离的转换模型,再通过比较各方向的传感器的数据,确定火焰的位置.最后将机械臂移动到着火点,控制喷水泵进行灭火.实验表明:系统能稳定运行,具有很好的通用性和推广前景.     

健康服务机器人室内导航视觉传感器研究与开发*

针对健康服务机器人室内导航的应用需求,设计出一种基于STM32F429的嵌入式导航视觉传感器,内嵌500万像素CMOS图像传感器和拥有高性能无线SOC的ESP8266,此次开发的视觉传感器,不是一般的CMOS图像传感器,而是综合了图像感知,图像处理,无线通信,节电唤醒等等功能于一体的智能化微型装置,即眼睛加大脑。经测试,本导航视觉传感器能够以每秒3次的速度向服务机器人发送位置,完全满足室内移动机器人的移动需求。     

The interaction between a robot and multiple people based on spatially mapping of friendliness and motion parameters

We aim to achieve interaction between a robot and multiple people. For this, robots should localize people, select an interaction partner, and act appropriately for him/her. It is difficult to deal with all these problems using only the sensors installed into the robots. We focus on that people use a rough interaction distance among other people . We divide this interaction area into different spaces based on both the interaction distances and sensor abilities of robots. Our robots localize people roughly within this divided space. To select an interaction partner, they map friendliness holding the interaction history onto the divided space, and integrate the sensor information. Furthermore, we developed a method for appropriately changing the motions, sizes, and speeds based on the distance. Our robots regard the divided spaces as Q-Learning states, and learn the motion parameters. Our robot interacted with 27 visitors. It localized a partner with an F-value of 0.76 through integration, which is higher than that of a single sensor. A factor analysis was performed on the results from questionnaires. Exciting and Friendly were the representatives of the first and second factors, respectively. For both factors, a motion with friendliness provided higher impression scores than that without friendliness.     

Determination of time to contact using a compound eye sensor

Recently, many control methods for autonomous robots based on biological mechanisms have been studied. In particular, the concept of time to contact, called the tau-margin, which is studied in ecological psychology, has attracted considerable attention. In conventional studies, various approaches have been used for the determination of the tau-margin, and the tau-margin has been used for the timing control of mobile robots. However, in these studies, robots fitted with a light bulb have been used, and direct light from the bulb has been used to determine the tau-margin. It is impossible to apply conventional methods to robots that use indirect light for determining the tau-margin. Therefore, we have developed a compound eye sensor comprising photodiodes. We employ the framework of optical flow to detect objects, and determine the tau-margin from the apparent size of the detected objects and the temporal changes in size. To demonstrate the effectiveness of the developed compound eye sensor, we control a mobile robot using this sensor, and conduct experiments in which the robot has to avoid an oncoming object. The timing of the robot is controlled by using the tau-margin. We conducted experiments by using an actual robot, and found that the robot could avoid an oncoming object successfully. We concluded that it is possible to determine the tau-margin by using the compound eye sensor which we have developed.     

Issues in the scaling of multi-robot systems for general problem solving

Problem solving using multi-agent robotic systems has received significant attention in recent research. Complex strategies are required to organize and control these systems. Biological-inspired methodologies are often employed to bypass this complexity, e.g. self-organization. However, another line of research is to understand the relationship between low-level behaviors and complex high-level strategies. In this paper, we focus on understanding the interference caused in multi-robotic systems for the problem of search and tagging. Given a set of targets that must be found and tagged by a set of robots, what are the effects of scaling the number of robots and sensor ranges? Intuitively, increasing robot numbers, or sensor strength would seem beneficial. However, experience suggests that path and sensor interference caused by increased robots, increased targets, and sensor range will be harmful. The following investigation uses several abstract models to elucidate the issues of robot scaling and sensor noise.     

Questionnaire-based social research on opinions of Japanese visitors for communication robots at an exhibition

This paper reports the results of questionnaire-based research conducted at an exhibition of interactive humanoid robots that was held at the Osaka Science Museum, Japan. The aim of this exhibition was to investigate the feasibility of communication robots connected to a ubiquitous sensor network, under the assumption that these robots will be practically used in daily life in the not-so-distant future. More than 90,000 people visited the exhibition. A questionnaire was given to the visitors to explore their opinions of the robots. Statistical analysis was done on the data of 2,301 respondents. It was found that the visitors’ opinions varied according to age; younger visitors did not necessarily like the robots more than elderly visitors; positive evaluation of the robots did not necessarily conflict with negative evaluations such as anxiety; there was no gender difference; and there was almost no correlation between opinions and the length of time spent near the robots.     

Multi-robot mobility enhanced hop-count based localization in ad hoc networks

The localization problem is important in mobile robots and wireless sensor network and has been studied for many years. Among many localization methods, the hop-count based approach is simple and scalable; however, the localization accuracy is not satisfactory if the node density is low. To solve this problem, in this paper a multi-robot approach is proposed to utilize the cooperation and mobility of the robots to improve the node distribution (density), thus enhancing the hop-count based localization. By an auction-based task allocation scheme, the robots can negotiate with the static sensor nodes and then select the most suitable robots to move to the area of sparse node density, thus increasing the localization accuracy for the static sensor nodes. On the other hand, the robots also can localize themselves with the help of the static sensor nodes. The efficacy of this approach is shown by simulation.     

A multi-robot positioning system using a multi-code ultrasonic sensor network and a Kalman filter

In multi-robot systems, each robot needs to have the position and pose information of itself and that of the other cooperative robots. This paper presents a synchronous distributed positioning system that uses a multi-code ultrasonic sensor network and a compensation algorithm using a Kalman filter. The bearings of robots are computed by using their position changes, and then compensated for by using the Kalman filter. The ZigBee sensor network protocol is used for communication among the robots and for the synchronization of the ultrasonic transmission timing. The experimental results show that our system positions multiple robots synchronously without any configured infrastructures. The results have a better accuracy and less accumulative error than those found in positioning systems without compensation.     

一种新型机器人三维力柔性触觉传感器的设计

基于柔性力敏导电橡胶材料,设计了一种能测量三维力的新型机器人柔性触觉传感器。研究了力敏导电橡胶材料的压阻效应,阐述了触觉传感器的设计思想,分别进行了触觉传感器单元设计和阵列结构设计和研究。获得了计算三维力的数学模型,并通过实验进行了三维力的验证。结果表明,设计的机器人三维力柔性触觉传感器具有设计简单,造价低廉,柔顺性好等优点,而且布置成阵列结构可用于医疗、体育、机器人等领域中检测三维力信息。     

An evaluation of machine guarding techniques for robot guarding

An increasing number of problems caused by the use of industrial robots should be expected due to the dramatically increasing population of robots today. In this paper, machine guarding functions and techniques are reviewed. The possibilities of transferring the concepts of machine guarding to robotic systems are then analyzed. The analysis shows that most of the guarding techniques are still applicable to industrial robots with modifications to accommodate differences. A set of guidelines for installing safeguarding devices is also presented. Research is need in the areas of guarding techniques for robotic cells, new sensor development, and sensor reliability, fusion, and coordination problems.     

Multi-observation sensor resetting localization with ambiguous landmarks

Successful approaches to the robot localization problem include particle filters, which estimate non-parametric localization belief distributions. Particle filters are successful at tracking a robot’s pose, although they fare poorly at determining the robot’s global pose. The global localization problem has been addressed for robots that sense unambiguous visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. Unfortunately, for robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new pose hypotheses across a wide region, in poses that may be inconsistent with previous observations. We introduce multi-observation sensor resetting (MOSR) to address the localization problem with sparse, ambiguous and noisy observations. MOSR merges observations from multiple frames to generate new hypotheses more effectively. We demonstrate experimentally on the NAO humanoid robots that MOSR converges more efficiently to the robot’s true pose than standard sensor resetting, and is more robust to systematic vision errors.